Label-free serum ribonucleic acid analysis for colorectal cancer detection by surface-enhanced Raman spectroscopy and multivariate analysis
Bibliographic record
Abstract
Studies with circulating ribonucleic acid (RNA) not only provide new targets for cancer detection, but also open up the possibility of noninvasive gene expression profiling for cancer. In this paper, we developed a surface-enhanced Raman scattering (SERS), platform for detection and differentiation of serum RNAs of colorectal cancer. A novel three-dimensional (3-D), Ag nanofilm formed by dry MgSO(4) aggregated silver nanoparticles, Ag NP, as the SERS-active substrate was presented to effectively enhance the RNA Raman signals. SERS measurements were performed on two groups of serum RNA samples. One group from patients, n=55 with pathologically diagnosed colorectal cancer and the other group from healthy controls, n=45. Tentative assignments of the Raman bands in the normalized SERS spectra demonstrated that there are differential expressions of cancer-related RNAs between the two groups. Linear discriminate analysis, based on principal component analysis, generated features can differentiate the colorectal cancer SERS spectra from normal SERS spectra with sensitivity of 89.1 percent and specificity of 95.6 percent. This exploratory study demonstrated great potential for developing serum RNA SERS analysis into a useful clinical tool for label-free, noninvasive screening and detection of colorectal cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".